Dynamic Timetable Scheduling using Multi-Agent Systems and Federated Learning

DOI

10.2991/978-94-6463-718-2_124How to use a DOI?

Keywords

Dynamic scheduling; multi-agent systems; federated learning; real-time adaptability; scalability; privacy-preserving AI; heterogeneous agents; decentralized collaboration; resource allocation; computational efficiency

Abstract

Dynamic timetable scheduling an important topic in the context of modern systems and needs suitable and efficient solution which are efficient flexible and privacy preserving in multiple environments. In this paper, we introduce a novel multi-agent systems and federated learning-based framework, and we theoretically ensure that our framework guarantees scalability, real-time adaptability, secure scheduling as well. It ensures an optimal resource allocation, mitigates communication lags, diverse agents and data privacy skills. The framework addresses hierarchical bottlenecks, is robust to data variability, and has computational efficiency appropriate for low-resource environments through decentralized agent-to-agent interactions. We validate it on real datasets, showcasing the applicability of our framework to various domains such as education, transportation, and healthcare. This work adds to dynamic scheduling, and is a scalable, privacy-preserving, adaptable middleware module suitable for real-time applications in complex, distributed environments.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - V. Sharmila
AU  - A. Rajivkannan
AU  - M. Venkatesan
AU  - S. Sangeetha
AU  - S. R. Sivani
AU  - V. Sumukhi
PY  - 2025
DA  - 2025/05/23
TI  - Dynamic Timetable Scheduling using Multi-Agent Systems and Federated Learning
BT  - Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
PB  - Atlantis Press
SP  - 1485
EP  - 1499
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-718-2_124
DO  - 10.2991/978-94-6463-718-2_124
ID  - Sharmila2025
ER  -